System and methods for indel identification using short read sequencing

ABSTRACT

Systems, methods, and analytical approaches for short read sequence assembly and for the detection of insertions and deletions (indels) in a reference genome. A method suitable for software implementation is presented in which indels may be readily identified in a computationally efficient manner.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 60/888,196, filed Feb. 5, 2007 and entitled “Finding IndelsUsing Short Sequencing Reads”, the entire contents of which are herebyincorporated by reference.

FIELD

The present teachings generally relate to systems, methods, and softwarefor analyzing sequence data and more particularly to systems, methods,and software for resequencing and identifying insertions/deletions usingshort read sequence data.

INTRODUCTION

High-throughput short read nucleic acid sequencing approaches continueto rapidly evolve and provide the potential for several orders ofmagnitude more sequence throughput than conventional Sanger sequencingmethods. Platforms and instrumentation capable of generating short readsequence information include by way of example; Applied Biosystems SOLiDinstrument platform, Solexa/Illumina 1G genome analysis system andothers. Such systems typically produce vast quantities of sequenceinformation with comparatively short lengths (on the order ofapproximately 10-50 based pairs) relative to Sanger-based approaches(approximately 500-1000 base pairs). Exemplary applications ofhigh-throughput short read nucleic acid sequencing approaches includeresequencing, gene expression analysis, and genomic profiling. Onedifficulty in utilizing data from short read sequencing platforms isthat modern assembly programs, data analysis approaches, and errorcorrection routines have been designed to operate with the longer readlengths found in conventional Sanger-based approaches and are not wellsuited for assembly of short read sequence information. For example, onedifficulty which is encountered when attempting to adapt conventionalsequence analysis approaches to short read sequence information is thatas the length of each individual read decreases, the probability that aread will occur more than once within a reference sequence increases.Additionally, complex genomes, such as mammalian genomes, often containmany repetitive sequences and it becomes increasingly challenging toassemble or relate short read information to the underlying or referencesequence. Thus a significant challenge in interpreting and analyzingshort read sequence information relates to mapping this information to arelatively large genome while reducing the number of errors and falsealignments.

SUMMARY

The present teachings are directed to systems, methods, and analyticalapproaches for interpreting short read sequence information includingnucleic acid fragment assembly. In one aspect, the present teachings maybe adapted for use in nucleic acid resequencing applications whererelatively large amounts of short read sequence information is availableand to be analyzed using reference sequence information. The methodsdescribed herein may advantageously be used to identify and locateputative insertions and deletions (e.g. indels) with respect to areference sequence or genome. The analysis approaches described hereinfurther reduce the time and computational complexity when allowing forindels within a reference sequence or genome. Additionally, the presentteachings may be adapted to reduce the number of false alignments andmapping errors arising when mapping short read sequence data toreference sequence data.

In various embodiments, a method of nucleic acid sequence analysis istaught. The method comprising the steps of: receiving nucleic acidsequence information comprising one or more mate pair sequences, whereinmate pair sequences comprise non-overlapping pairwise sequencesseparated by an intervening sequence length; receiving nucleic acidsequence information comprising at least one reference sequence;performing a mapping operation for each of the one or more mate pairsequences in which the non-overlapping pairwise sequences are aligned tothe at least one reference sequence by the steps of: performing a firstmapping operation aligning the non-overlapping pairwise sequences to theat least one reference sequence with a selected mismatch constraintidentifying non-overlapping pairwise sequences for which one of thenon-overlapping pairwise sequences align to the at least one referencesequence while satisfying the selected mismatch constraint; performing asecond mapping operation designating a window region of the referencesequence to align the non aligned pairwise sequence with a selectedmismatch constraint; identifying non-overlapping pairwise sequences thatare successfully mapped following performing the first and secondmapping operations; and, outputting the results of the mappingoperations.

In other embodiments, a system for nucleic acid sequence analysis istaught. The system further, comprising: a data analysis unit configuredto: receive nucleic acid sequence information for one or more mate pairsequences, wherein mate pair sequences comprise non-overlapping pairwisesequences separated by an intervening sequence length and furtherconfigured to receive nucleic acid sequence information for at least onereference sequence; perform a mapping operation for each of the one ormore mate pair sequences in which the non-overlapping pairwise sequencesare aligned to the at least one reference sequence by the steps of:performing a first mapping operation aligning the non-overlappingpairwise sequences to the at least one reference sequence with aselected mismatch constraint identifying non-overlapping pairwisesequences for which one of the non-overlapping pairwise sequences alignto the at least one reference sequence while satisfying the selectedmismatch constraint; performing a second mapping operation designating awindow region of the reference sequence to align the non alignedpairwise sequence with a selected mismatch constraint; identifynon-overlapping pairwise sequences that are successfully mappedfollowing performing the first and second mapping operations; and, adata terminal for displaying the results of the mapping operationsgenerated by the data analysis unit to a user.

In still other embodiments, a computer-readable medium readable toexecute a method of nucleic acid sequence analysis is taught. Thecomputer-readable medium capable of executing the method comprising:receiving nucleic acid sequence information comprising one or more matepair sequences, wherein mate pair sequences comprise non-overlappingpairwise sequences separated by an intervening sequence length;receiving nucleic acid sequence information comprising at least onereference sequence; performing a mapping operation for each of the oneor more mate pair sequences in which the non-overlapping pairwisesequences are aligned to the at least one reference sequence by thesteps of: performing a first mapping operation aligning thenon-overlapping pairwise sequences to the at least one referencesequence with a selected mismatch constraint identifying non-overlappingpairwise sequences for which one of the non-overlapping pairwisesequences align to the at least one reference sequence while satisfyingthe selected mismatch constraint; performing a second mapping operationdesignating a window region of the reference sequence to align the nonaligned pairwise sequence with a selected mismatch constraint;identifying non-overlapping pairwise sequences that are successfullymapped following performing the first and second mapping operations;and, outputting the results of the mapping operations.

DRAWINGS

The skilled artisan will understand that the drawings, described below,are for illustration purposes only. These figures are not intended tolimit the scope of the present teachings in any way.

FIG. 1: illustrates an exemplary double stranded DNA fragment andassociated mate pair primers (F3) and (R3) according to the presentteachings.

FIGS. 2A & B: illustrate the occurrence and relative size of insertionsand deletions in a comparison between a reference fragment and asequencing fragment according to the present teachings.

FIGS. 3A & B: illustrate flowcharts for mate pair sequence analysisaccording to the present teachings.

FIG. 4: illustrates the application of a search region/window scanningtechnique for use with mate pair sequence analysis according to thepresent teachings.

FIG. 5: illustrates a large insert identification approach according tothe present teachings.

FIG. 6: illustrates exemplary validation and simulation results for matepair analysis according to the present teachings.

FIG. 7: illustrates an exemplary system for performing nucleic acidsequence analysis according to the present teachings.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not intended to limit the scope of the current teachings. Inthis application, the use of the singular includes the plural unlessspecifically stated otherwise. For example, “a forward primer” meansthat more than one forward primer can be present; for example, one ormore copies of a particular forward primer species, as well as one ormore different forward primer species. Also, the use of “comprise”,“contain”, and “include”, or modifications of those root words, forexample but not limited to, “comprises”, “contained”, and “including”,are not intended to be limiting. The term “and/or” means that the termsbefore and after can be taken together or separately. For illustrationpurposes, but not as a limitation, “X and/or Y” can mean “x” or “Y” or“X and Y”.

The section headings used herein are for organizational purposes onlyand are not to be construed as limiting the described subject matter inany way. All literature and similar materials cited in this application,including patents, patent applications, articles, books, and treatisesare expressly incorporated by reference in their entirety for anypurpose. In the event that one or more of the incorporated literatureand similar materials defines or uses a term in such a way that itcontradicts that term's definition in this application, this applicationcontrols. While the present teachings are described in conjunction withvarious embodiments, it is not intended that the present teachings belimited to such embodiments. On the contrary, the present teachingsencompass various alternatives, modifications, and equivalents, as willbe appreciated by those of skill in the art.

In various embodiments, nucleic acid sequence information that may beadapted for use with the present teachings comprises mate-pair sequenceinformation FIG. 1 illustrates an exemplary double stranded DNA fragment50 and associated mate pair primers (F3)100, (R3)102. In a typicalanalytical paradigm, sequence information may be obtained by sequencingfrom both directions 120, 125 (e.g. 3′ and 5′ portions) of a nucleicacid fragment. For example, as shown in the illustration, primer pairsF3 and R3 span a portion of the nucleic acid fragment 50 with anintervening distance 105 of a known or approximated size 150. Suchpair-wise sequence information, referred to as mate-pair information,generally constrains the placement and size of the sequencing readswithin a sequence to be assembled 140. As used herein, the term “matepair” may include fragment information for both ends of an insert incombination with the insert's size 150, such that the distance orsequence length separation between the two sequenced fragments is knownto at least a first approximation.

In one aspect, short read mate-pair sequence information may be obtainedby sequencing a portion of the nucleic acid fragment 50 using primerpairs F3/R3 such that sequence information is obtained correspond to aforward locality (3′) 100 and distal locality (5′) 102 with anintervening region 105. Typically, the sequence of the interveningregion 105 is not provided for by the sequence information correspondingto the forward locality (3′) 100 and distal locality (5′) 102, however,the size or number of bases spanning the intervening region 105 may beapproximately or accurately known (e.g. the sequence informationobtained using the F3/R3 primer pairs does not overlap with respect tothe reference sequence but rather is separated by the interveningsequence). As previously discussed, short read sequence information istypically significantly shorter in comparison to other sequencingmethodologies such as Sanger sequencing based approaches. In one aspect,short read sequence information comprises sequence information from eachprimer F3/R3 having a size from between approximately 10-30 base pairs.In another aspect, short read sequence information comprises sequenceinformation from each primer F3 R3 having a size from betweenapproximately 25-50 base pairs. In another aspect, short read sequenceinformation comprises sequence information from each primer F3/R3 havinga size from between approximately 40-75 base pairs. It will beappreciated however that the application of the present teachings may besuitable for use with other sequence lengths and that sequence lengthsarising from F3/R3 need not be identical to one another.

For mate pair reads the distance 105 between primers F3/R3 may vary fromone primer pair to the next. In accordance with the present teachings,paired short read sequence information is generally separated by adistance 105 of approximately a few kilobases (Kb). For example, thedistance between primers F3 100/R3 102 may reside between approximately2-3 Kb, between approximately 4-7 Kb, or between approximately 8-15 Kb.It will be appreciated however that the application of the presentteachings may be suitable for use with other mate pairs reads withdistances between primers F3 100/R3 102 greater or less than thoselisted above.

Methodologies for generating mate pair sequence information inaccordance with the above-indicated sizes are known in the art. Forexample, United States Patent Application Publication number2006/0024681 entitled “Methods For Producing A Paired Tag From A NucleicAcid Sequence And Methods Of Use Thereof” describes various approachesfor developing nucleic acid libraries suitable for use with mate pairsequencing approaches the contents of which are hereby incorporated byreference in their entirety. In contrast, U.S. Pat. No. 6,714,874entitled “Method and System For The Assembly Of A Whole Genome Using AShot-Gun Data Set” describes methods and systems for assembling a genomefrom a shot-gun set of end sequenced DNA fragments using larger DNAfragments and sequencing information as compared to short read sequenceinformation, the contents of which are hereby incorporated by referencein their entirety. Previous work relating to software tools foranalyzing mate pairs assemblies in the context of larger nucleic acidfragments and whole genome shotgun assemblies include “A Tool ForAnalyzing Mate Pairs In Assemblies (TAMPA)”, Dew et al., Journal ofComputational Biology, Vol 12, No. 5, 2005 and in the context of shorternucleic acid fragments include “An Analysis Of The Feasibility Of ShortRead Sequencing”, Whiteford et al. Nucleic Acids Research, Vol 33, No.19, 2005 and “Fragment Assembly With Short Reads”, Chaisson et al.,Bioinformatics Vol 20, No. 13, 2004 the contents of which areincorporated by reference in their entirety. In view of these articles,it is observed that potential problems and pitfalls exist when applyingconventional sequence assembly techniques to short read sequence data.

As shown in FIGS. 2A and 2B mate pair sequencing applications accordingto the present teachings can be used to detect or identify insertionsand deletions of varying indel sizes. For relatively large size indels,one approach to indel identification can be determined as the deviationfrom the average distance which can be used to derive the existence andsize of an indel. For the purposes of the present teachings the size oflarge indels is considered to include those sized approximately 0.2 Kband greater. In various embodiments, a further advantage of the presentteachings is that the disclosed methods can be used to identifydeletions, including those with sizes of up to approximately 1 Kb, aswell as insertions As will be described in greater detail hereinbelow amini-assembly approach may be used in the detection/identificationprocess.

According to the approach described above, larger indels can begenerally identified by this methodology. Small to medium size indels,however, present a more challenging problem when using mate pairsequence information and thus other potentially more efficientalternatives are discussed. For the purposes of the present teachingsthe size of medium indels is considered to reside between approximately10 bp and 200 bp for deletion, and 5 bp and 200 bp for insertion.Likewise, for the purposes of the present teachings the size of smallindels is considered to reside between approximately 1 bp and 10 bp fordeletions and 1 bp and 5 bp for insertions.

In various embodiments, small to medium size indels may be identifiedusing mate pair sequencing reads and alignments directly. The approachtaken however, places certain constraints on the alignment process so asto reduce the computational complexity of the analysis. The size andnumber of short read sequence data is such that unconstrained alignmentsof short reads to reference sequences that allow for gaps for insertionsand/or deletions create intractable and time consuming problems.Furthermore, an unconstrained mapping typically leads to numerouspotential false alignments and reduces the overall quality andconfidence in the analysis. The approach of the present teachingsovercomes this obstacle imposing certain constraints on themapping/alignments so as to improve the result quality while reducingthe computational complexity of the process. In the instance of pairedread information as provided by the mate pair sequence information wherethe approximate distance between the tags/primers is know, it isincreasingly likely that at least one of the two reads in the pair willnot overlap with an indel and consequently such a characteristic may beadvantageously utilized to reduce or limit the number of falsealignments.

The flow diagram 300 depicted in FIG. 3 illustrates a stepwise analysisapproach that may be used to identify small and medium size indels inaccordance with the present teachings. Commencing in state 305 mate pairsequence information (e.g. tags) including reference sequenceinformation is received or collected and prepared for further analysis.Such mate pair sequence information may comprise, for example, basesequences associated with one or more mate pair primer sets (e.g. F3/R3primers as shown in previous illustrations). The base sequenceinformation may also include additional information including forexample quality or confidence values, reference sequence localityinformation, annotations indicating the exact or approximate distancebetween the mate pair primers, and other information. Reference sequenceinformation may comprise various types of base sequences for example forone or more target genomes or samples of interest. It will beappreciated by one of skill in the art that the type of mate pair andreference sequence information used in the analysis may be obtained innumerous manners. For example such information may be transmitteddirectly from a sequencing instrument platform or retrieved from adatabase used for storing such information. The mode and manner ofcollecting, transmitting, retrieving, processing, and/or preparingsequencing data for use with the analysis approaches of the presentteachings is not to be construed as limiting to the scope of the presentteachings.

Once appropriate sequence information has been collected in state 305 amapping/aligning operation is performed in state 310. The mappingoperations performed herein are generally shown in previousillustrations and are used to determine the appropriate positioning ofthe mate pairs with respect to the reference sequencers). It will beappreciated that such mapping operations can be conducted in asubstantially automated manner without user interaction or alternativelyvarious graphical user presentations of the mapping may beprogrammatically implemented.

The mapping operations continue in state 315 where the mate pairanalysis determines the positioning of the mate pairs with respect tothe reference genome allowing for a selected number or range ofmismatches between each mate pair sequence (e.g. F3/R3). As oneexemplary range of mismatch tolerances, between 0-2 mismatches may bepermitted for a short read mate pair sequence. Such a mismatch toleranceis sufficiently stringent to aid in accurate identification of theappropriate placement of each mate pair with respect to the referencesequence. Furthermore, providing a permissible mismatch number or rangeallows for a degree of error tolerance in the sequence informationand/or the alignments to facilitate determination of the most likelypositioning of the mate pairs with respect to the reference sequence. Aswill be described in greater detail hereinbelow, it is possible thateither none, one, or both mate pairs may align with the referencesequence.

In performing the initial mapping in state 320, a determination is madeas to whether either F3 and/or R3 can be mapped to the referencesequence given the permissible mismatch tolerances described above. Asshown by outcome 330, in the instance that a single mate pair can bemapped to the reference sequence (e.g. F3 can be mapped but R3 cannot) awindow region distance determination is made in state 340. The windowregion distance denotes a range of bases that deviate from theapproximate or expected distance between the mate pair primers but whichmay account for the presence of additional bases resulting from one ormore insertions or the absence of expected bases between the mate pairsresulting from a deletion.

An exemplary window region distance and associated mate pair scanningand alignment approach is shown in FIG. 4. In one exemplary instance, amate pair sequence (R3 in the upper portion of the diagram and F3 in thelower portion of the diagram) is successfully mapped to the referencesequences given the previously described mismatch tolerances. Based onthe size of each mate pair and the approximate or expected distancebetween the mate pairs, a window of a selected base sequence number orrange is identified for which to attempt to align the corresponding matepair sequence to (F3 in the upper portion of the diagram and R3 in thelower portion of the diagram). It is within this region that the methodreferred to in FIG. 3 attempts to map the corresponding mate pair. Itwill be appreciated by one of skill in the art that such an approachallows for a degree of flexibility in mapping to account for insertionsand/or deletions with respect to the reference sequence which mightotherwise prevent alignment of the mate pair sequences with theapproximated or expected intervening sequence length.

Referring again to FIG. 3, in state 350 the mate pair tag (e.g. R3 inFIG. 3A state 350) which could not readily be mapped to the referencesequence is aligned within the window region. During this process, thealignment of the mate pair tag accommodates alignment with and/orwithout mismatches of a specified number or amount in the sequenceinformation. In one aspect, such an approach provided for an “anchor” inthe mapped mate pair tag (e.g. F3) and permits a sliding or flexiblemapping of the unmapped mate pair tag (e.g. R3).

In state 360, if a successful mapping of the previously unmapped matepair tag (e.g. R3) is accomplished, the mapping of both mate pair tagsand associated sequence information may be identified and outputted instate 370. If the previously unmapped mate pair tag (e.g. R3) could notbe mapped within the window region according to the previous step, thenthe lack of mapping may be identified and output in state 375.

Reverting again to the initial mapping performed in state 320,determining whether either F3 and/or R3 can be mapped to the referencesequence. As shown by outcome 335, in the instance that both mate pairtags can be mapped to the reference sequence (e.g. both F3 and R3 can bemapped) a distance constraint determination is made in state 345.Verifying that the distance between the mate pairs matches the expectedor approximated distance between the mate pair tags significantlyreduces the likelihood of false mappings. Such a determination alsodesirably avoids false positive conditions which would otherwise occurwith greater frequencies relative to longer conventional sequencingreads such as those provided by Sanger sequencing applications. Thus instate 370, if both mate pair tags (F3 and R3) are successfully mappedand distance constraints between the two are met the sequenceinformation and results may be identified and output as described above.Likewise, for mate pair tags which fail to satisfy the distanceconstraint conditions, the results may be output in state 375 asdiscussed previously.

FIG. 3B provided an analogous analytical approach for mate pair sequenceanalysis wherein the mate pair tag which is successfully mapped in state370 is the opposing mate pair tag (e.g. R3 is mapped as compared to F3).Those of skill in the art will appreciate that the implementation of theanalytical approach would proceed in a similar fashion with expectedoutcomes as indicated in the Figure. Furthermore, it will be appreciatedthat these analytical approaches may be readily implemented and/or codedusing various software packages and/or programming languages and as suchall such implementations are conceived to be within the scope of thepresent teachings.

In various embodiments, the above-described analytical approach forindel identification can be summarized into two principle stages. In thefirst stage the mate pair tags or reads may be aligned allowing for asmall number of mismatches and in the second stage, for pairs of matepair tags or reads in which only one mate pair tag is successfullymapped, an attempt may be made to align the non matching second matepair tag to allow a permissible gap in the read but still constrain thealignment to the relatively small region. The location of the region maybe considered as relative to the location of the first tag that mappedto the reference sequence and may be determined by the size of theclones in the library.

FIG. 5 illustrates an approach to determining an insertion using amini-assembly approach. This approach may be readily utilized whenattempting to identify a relatively long insertion 505 (rather than aduplication of another region) that may be present in a genome orsequence of interest 510. In the illustration, the insertion 505 in theexemplary sequence 510 is shown as the thickened line. Reads areindicated by shorter lines 515 under the exemplary sequence 510. It ispossible to assemble the reads to cover 520 the insertion 505. In thisexample, at the junction 525 of the insertion either the first half orthe second half of the reads 515 match the reference sequence 510.

Using this approach to assembly, a rule that there exist a perfect matchfor at least N bases of the overlapping region between reads may beenforced. For those reads matching this conditions, the shortest path inthe overlapping group of reads may be traversed to identify theinsertion.

FIG. 6 illustrates two tables of statistical results for theabove-described sequence analysis approaches. The results presented inTable 1 provide observed false positive rates when detecting deletionsand insertions according to the mate pair analysis approach of FIG. 3.To derive these numbers approximately 1 million paired reads from the C.elegans genome where used adding 10% uniform random sequencing errors tothe read with a read length of approximately 25 bp. For this experiment,2 mismatches where allowed in accordance with step 315 and M mismatchesallowed with at least 8 bp on either side of the indel in accordancewith step 350 in FIGS. 3 A/B.

The mapping and indel finding approach described herein was then appliedto this set of reads and subsequently the number of identified indelswas counted. False positive rates correspond to the portion of pairsthat find an indel using the approach described in FIG. 3. Falsepositives for deletions are shown in Table 1 and insertions shown inTable 2. It will be appreciated that since the reads are sampled fromthe reference sequence without any indels, this simulation estimates thefalse positive rate. For results shown in Table 1, the number ofdeletions is presented in column 1A and it can be observed that when thereads are aligned without mismatch, deletions of up to approximately 100bp can be found with a substantially low false positive rate. Where D isthe maximum size of deletion allowed in the approach set for in FIG. 3.Similarly, the results for insertions is shown in Table 2 are evenbetter than that for deletions presented in Table 1. Where I is themaximum size of an insertion allowed in the approach set for in FIG. 3.Thus, using only the alignment of reads, insertions of up toapproximately 8 bp can be readily identified.

One significant aspect of the present teachings is that by theirapplication, one may advantageously align pairwise short read sequenceinformation with a greater degree of confidence and accuracy as comparedto conventional approaches. For example, in the case where one of thetwo mate pair sequences does not readily align to the reference sequenceas a result of the presence of an indel within the intervening sequencethe window scanning approach may be used to “rescue” the non-alignedmate pair sequence and determine an appropriate positioning of thissequence with respect to the reference sequence. It will be appreciatedthat applying these techniques thus improves utilization of the matepair sequence information and avoids undesirable “loss” of non-alignedmate pairs that result from the presence of indels.

Likewise, the miniassembly approach demonstrated by the presentteachings provides an efficient manner in which to “span” an insertionthereby permitting suitable alignment of the mate pair sequences whileidentifying the presence of the insertion with respect to the referencesequence.

FIG. 7 illustrates an exemplary system 700 which may be used to performsequence analysis and indel detection according to the aforementionedmethods. In one aspect, a sample processing component 705 may providemeans for performing operations associated with sample processing anddata acquisition. These operations may include by way of example;labeling, amplifying, and/or reacting the sample in the presence of asuitable marker or label, exposing the sample to an appropriate analysissubstrate or medium; and detecting signals or emissions from the samplewhich will serve as input data for the sequence analysis and indeldetection methods. Instruments which may be associated with theseoperations include but are not limited to array-analysis instruments,sequencing instruments, fluorescent signal detection instruments,thermalcyclers, and other such instruments used in sample processing anddata acquisition.

Raw data provided by the sample processing component 705 may besubsequently stored in a data storage component 715. This component 715may comprise any of various types of devices designed for storing ofdata and information including for example; hard disk drives, tapedrives, optical storage media, random access memory, read-only memory,programmable flash memory devices and other computers or electroniccomponents. Furthermore, the data and information obtained from thesample processing component 705 may be stored and organized in adatabase, spreadsheet, or other suitable data structure, data storageobject, or application which operates in connection with the datastorage component 715.

In various embodiments, a data analysis component 710 may be presentwithin the system 700. This component 710 possesses functionality foracquiring data and information from the sample processing component 705or the data storage component 715. The data analysis component 710 mayfurther provide a hardware or software implementation of theaforementioned sequence analysis and indel detection methods. In oneaspect, the data analysis component 710 is configured to receive inputdata and may return processed data including sequence analsysis andindel detection information which may be stored in the data storagecomponent 715 or displayed directly to the investigator via a displayterminal 720.

Each of the functionalities of the aforementioned components 705,710,715,720 may be integrated into a singular hardware device or into one ormore discrete devices. These devices may further possess networkconnectivity facilitating communications and data transfer between thedevices as desired by the investigator. It will be appreciated thatnumerous suitable hardware and software configurations may be developedwhich implement the sequence analysis and indel detection methods of thepresent teachings, as such each of these configurations should beconsidered but other embodiments of the present teachings.

Exemplary Analysis and Statistical Treatment

According to the analytical approach of the present teachings the methodfor finding indels may comprise initially mapping reads requiringsubstantially high similarity (allowing for a relatively high degree ofstringency such as either a perfect match or 1 mismatch). Thereafter,each pair of reads in which only one tag maps uniquely or with a highdegree of confidence the other corresponding tag is aligned toaccommodate indels and/or more mismatches in the region that issubstantially the correct distance away from the mapped tag. In variousembodiments, this distance is determined by the library insert size andits variation.

In evaluating the above-described methods the probability that a randomsequence can achieve the same alignment may be considered in a mannersimilar to the statistical significance analysis of local alignment. Inone aspect, using an independent random sequence model, relatively lowfalse positive rate may be observed (alignment from random sequence)such that approximately 3 mismatches and one deletion of up to 30 bpsmay be accommodated. For insertions, a 10 bp insertion may be readilyidentified when allowing no mismatch.

This analysis suggests that alignment is not random. Upon furtherconsideration, for an alignment with a deletion, for example, therecould be two possible hypothesis, one is that the deletion is real, theother is that the read is the result of sequencing errors.

Testing these hypotheses the probabilities the read supports eitherhypotheses may be evaluated from the probability of observing the readunder the two hypotheses (one with an indel, the other without), andthen decide when to accept the indel. The analysis suggests that indelsthat reside in the middle of the read are of high confidence.

Combining these analyses with other results, for a given genome size, acertain coverage rate, a read length, and a sequencing error rate, thechance of finding an indel can be estimated. For example, the chance offinding a deletion of up to 30 bp with sufficient confidence is1−(1−P)̂(kL), where k is sequencing coverage, P=xy(R−10)/L, where x isthe probability that a read has at most 3 sequencing errors, y is theprobability the paired read has at most 1 mismatch, R is the read lengthand L is the genome length.

It will be appreciated that the problem of aligning short reads to areference sequence while allowing gaps for insertions and/or deletionsby conventional means leads to an unacceptably time consuming processand may lead to numerous false alignments. According to the presentteachings, by using paired reads where the approximate distance betweenthe two tags are known, it can be determined that at least one of thetwo reads in the pair will not overlap an indel and this consequentlywill limit the false alignments.

According to the present teachings, indel detection/identification canproceed using reads that are aligned allowing for a small number ofmismatches and for pairs of reads in which only one tag mapped,attempting to align the non matching second tag allowing a gap in theread while constraining the alignment to the relatively small region R.The location of R is relative to the location of the first tag thatmapped to the reference sequence and may be determined by the size ofthe clones in the library. The size of R may be indicated by |R| and ifthere is no confusion P may be used in place of |R|. Note that the sizeof R may be determined by the range of the insert size rather than bythe insert size itself.

Bayesian Approach and Relation to False Discovery Rate

One manner in which to assess whether the alignments found aboverepresent real indels is to assess how likely it is that two randomsequences can form such an alignment. Such an approach is similar to thestatistical significance estimate that may be conducted for localsequence alignment. However, such a measure does not take into accountthe likelihood of the existence of an indel. Even if random sequenceshave a low chance of forming an alignment, it does not necessarilyindicate that the indel is real and thus there could be otherexplanations such as sequencing errors.

Applying a Bayesian approach to estimate the confidence that analignment is real may be useful to test the utility of such ananalytical approach to sequence analysis. When a read (or reads incontext of a mini-assembly as will be described in greater detail hereinbelow) aligns to an indel, there are two potential scenarios: (1) thereis a real indel at this position of the reference sequence; and thereads in the alignment arise from this indel region (H1); (2) there isno indel at this position, and the reads in the alignment arise fromanother region and form the observed alignment due to sequencing errors(H0). Estimating P(H0|reads) and P(H1|reads) where “reads” is the set ofreads that align to the indel P(H1|reads)/P(H0|reads) may be used as ameasure of confidence that the reads find a real indel. In one exemplarymanner, when the measure is larger than 10, H1 may be accepted, e.g.that the indel is real. Restated another way this enforces that forevery 11 (10+1) indels that are determined to be real, approximatelyonly 1 will be false.

Using a Bayes' theorem approach, P(H1|reads)=P(reads|H1)P(H1)/P(reads)and P(H0|reads)=P(reads|H0)P(H0)/P(reads). As discussed above, the ratioP(H1|reads)/P(H0|reads) is of interest, which isP(reads|H1)P(H1)/P(reads|H0)/P(H0). In practice, indels are relativelyrare, P(H1) is typically much smaller than P(H0) and only the ratioP(H1)/P(H0) is important. For simplicity, set P(H0)=1 and therefore,P(H1|reads)/P(H0|reads)=P(reads|H1)P(H1)/P(reads|H0). In the followinganalysis the three above terms are estimated and evaluated.

The following assumptions and lemmas may be used in the statisticalanalysis.

Assumption 1 The reference sequence follows a generally uniformindependent random distribution, e.g. each base of the sequence isindependently distributed with A, C, G and T each having a probabilityof approximately ¼.

Assumption 2 Sequencing errors are generally uniformly distributed withan error rate of p per base; and generally only cause mismatches.

Note other more general sequencing error models can readily be used withthe presented results adapted thereto.

Assumption 3 (Uniform shotgun sampling) Each read r is generally equallylikely to be sampled from any position of the reference sequence. Forpaired reads, the region where one read is from is known, the read maybe assumed to be sampled uniformly in that region.

It will be appreciated that this assumption may be used for simplicityof discussion and other known biases can be readily incorporated.

Lemma 1 Let r be a read and R a sequence. The chance that r is a readfrom sequence R is: P(r|R)=ΣjP(r|Rj)P(Rj), where Rj is the event thatread r is sampled from R starting at position j.

Lemma 2 Let r be a read and R a sequence. If |R|=|r| and r and R have xmismatches and y matches when aligned to each other, it can be estimatedthat P(r|R)=p^(x)(1−p)^(y).

Lemma 3 Let r be a read and R a sequence, then E(P(r|R))=(1/4+p/2)^(|r|)

Proof. From Lemma 1,E(P(r|R))=E(ΣjP(r|Rj)P(Rj))=|R|E(P(r|Rj)P(Rj))=E(P(r|Rj)) sinceP(Rj)=1/|R|, and each P(r|Rj) is generally identically distributed.

From lemma 2,

$\begin{matrix}{{E\left( {P\left( r \middle| {Ri} \right)} \right)} = {\sum\limits_{j}{\begin{pmatrix}{r} \\j\end{pmatrix}\left( \frac{1}{4} \right)^{j}\left( \frac{3}{4} \right)^{{r} - j}{p^{{r} - j}\left( {1 - p} \right)}^{j}}}} \\{= \left( {{\frac{1}{4}\left( {1 - p} \right)} + \frac{3p}{4}} \right)^{r}} \\{= {\left( {\frac{1}{4} + \frac{p}{2}} \right)^{r}.}}\end{matrix}$

Noting the following useful observations.

Observation 1, from Lemmas 1 and 2, P(reads|H0) can be estimated byaligning r to each position of a region that is the correct distance andorientation from the location anchored by the corresponding tag. Whenthis is time consuming, it can be estimated by only finding significantalignments (with fewer mismatches), and use Lemma 2 on those alignments,and for all the other positions, use the expected probability from Lemma3.

Observation 2, one aspect of the analysis below may be to determine thetype of alignment that is used to confidently predict an indel. It isdesirable to determine the requirement for the alignments such that whenH1 is true, the reads that are found are likely to have a high value forP(H1|reads)/P(H0/reads). In this instance P(reads|H1) may be estimatedsince the reads are aligned to the indels. However, P(reads|H0) isharder to estimate since there is usually no known alignments for thosereads to the reference sequence. However, the average analysis may beused to estimate P(reads|H0) and obtain the necessary requirement forthe alignments (e.g. size of indel, number of mismatches, size ofoverlap etc) in order to accept it as a potential indel. To identify anindel, observation 1 may be used to estimate P(reads|H0) for eachindividual case.

Finding Deletions

Evaluating what type of alignment can lead to a confident prediction ofthe existence of a deletion one can focus on an alignment with exactlyone deletion. Two parameters can be used describe the alignment: thesize of the deletion D and the number of bases X in the shorter of thetwo sequences flanking the deletion. Evaluating what constraints on Dand X are satisfied in order to confidently predict a deletion as wellas determining the typical size of a deletion to be found can behelpful.

In one exemplary case, where P(r|H0) is relatively large and acceptingthis as a deletion may lead to false positive identification. Using aprior P(H1)=10⁻⁴ D(S), meaning there is approximately 1 deletion every10000 bases and the distribution of length is given by D(S). It isbecomes increasingly difficult to estimate D(S) and thus for simplicityD(S)=2^(−S), the geometric distribution may be used.

Consequently:

P(H1|read)/P(H0|read)=10⁻⁴2^(−S) P(read|H1)/P(read|H0)

From Lemma 2, P(read|H1)≦p^(m)(1−p)^(L−m)/R where L is the read lengthand m is the number of mismatches in the deletion alignment. ForP(read|H0), if the shorter flanking sequence has n mismatches, thenestimate P(read|H0)=p^(n)(1−p)^(L-n)/R, assuming more mismatches on theother flanking sequence.

For the typical case, on average, there are approximately 3X/4mismatches and half of the m mismatches are in the second half of theflanking sequence. In this typical case, from Lemmas 2 and 3,P(read|H0)=p̂(3X/4+m/2) (1−p)̂(L−3X/4−m/2)/R+f, where f=(0.25+p/2)^(L).

Since f is generally negligible compared to the other term.

$\begin{matrix}{{{P\left( {H\; 1} \middle| {read} \right)}/{P\left( {H\; 0} \middle| {read} \right)}} = {{p^{\frac{m}{2} - \frac{3X}{4}}\left( {1 - p} \right)}^{\frac{3X}{4} - \frac{m}{2}}10^{- 4}2^{- S}}} & (1)\end{matrix}$

For m=2 and L=25, p=0.05 (real sequencing error rates may be evensmaller), solving P(H1|read)/P(H0|read)>10 using the average formula forP(H0|read), yields X>10 when S=10. This suggests that to a deletion ofup to 10 bases one can be placed when the deletion is in the approximatemiddle 5 bases of a read. One practical approach is to first find thedeletion alignment, check the two flanking sequences to get the value nfor the formula P(H1|read)/P(H0|read) and then directly check whetherthe ratio is larger than 10. Also it may be noted that when there ismore than one alignment in the region, a hypothesis can be formed foreach alignment and thereafter check P(H|read) for each hypothesis.

The above discussion suggests that a single deletion alignment is enoughto confidently find a deletion in the range of approximately 10 bases.When multiple reads match the same deletion, one may confidently findlonger reads. With the same 0.05 error rate and L=25, if two reads alignto the same deletion in the middle 5 bases then a deletion up to nearly40 bases may be readily found. It will be appreciated thathigh-throughput sequencing platforms such as the Applied BiosystemsSOLiD instrument may generate approximately 50 to 100× coverage, and onemay expect 10 or more reads to cover the deletion locality thusproviding the opportunity to find larger deletions of 300 bps or longer.

Estimating the probability that each deletion can be found using thedeletion alignment and detecting criteria above a single read with atmost 2 sequencing errors to cover the junction with the middle 5 basesof the read is sufficient. Letting G be the genome length and supposinghave KG total good pairs of reads of 25 bps (base coverage of 50KX). Inone aspect, a good pair suggests that both reads have at most 2sequencing errors. For any deletion, the chance of finding one read thatcovers it in the middle 5 bases is 1−(1−5/G)^(2KG)≈1−e^(−10K) when G islarge.

For K=1, 0.5 and 0.25 and large G, the chances of finding a deletion are99.995%, 99.32% and 91.79%, respectively. The number of raw reads may beestimated and is useful to achieve this number of good pairs. At K=0.25level, approximately 12.5× base coverage of good pairs would suffice. Itmay also be noted that when a deletion is not placed in the middle 5 bpof a read, one may still confidently identify it if there are multiplereads covering it near the bordering regions.

Finding Insertions

Finding Insertions with Alignment Alone

In various embodiments, an analysis of insertion alignments can beperformed that is similar to that for deletion alignments except that aread that includes an insertion can only have part of it mapped to thereference sequence giving rise to a higher rate of false positives.

Where an insertion alignment occurs, two hypotheses H0 and H1 may beformed as done previously. To estimate P(H1), it can be estimated thatat each position, there is 10⁻⁴ chance of having an insertion and thereis a geometric distribution for the length of the insertion. For aninsertion of length S, each of the 4^(S) sequences of length S isgenerally equally likely to be inserted.

Accordingly P(H1)=10⁻⁴8^(−S)

Therefore P(read|H1)=p^(m)(1−p)^(L−m)/R

Defining f as the expected probability of observing a read in the regionof R bases, and from Lemma 2, gives f=(0.25+p/2)^(L). Using the averagenumber of mismatches as before, therefore

P(read|H0)=max(f,p ^(3L/4−3X/4+m/2)(1−p)^(3X/4−m/2+L/4) /R),

where X is the length of the longer of the two flanking regionssurrounding the insertion.

From above,

P(H1|read)/P(H0|read)<p ^(m)(1−p)^(L−m) P(H1)/(fR).

If m=1, L=25, R=3000, and p=0.1, P(read|H1)/P(read|H0)<3.14×10³8^(−S)and it is less than 10 when S>2.76. Therefore, at a sequencing errorrate of 0.1, to confidently find an insertion, the length of theinsertion should be at most 2. Lowering the error rate to 0.05, theabove calculation leads to the conclusion that the size of the insertionshould be smaller than 6 for confident detection is this example.

It is clear that, when p=0.1 and X≧14,q(X):=p^(3L/3−3X/4+m/2)(1−P)^(3X/4−m/2+L/4)/R>f. SolvingP(H1|read)/P(H0|read)>10, X≦14 when S=2 and X≦16 when S=1, so theshorter flanking region should be at least 9 or 8 bases long,respectively. When p=0.05, a similar calculation leads to q(X)>f whenX≦16 and it can concluded that the shorter flanking region should be atleast 6 bases long.

The above calculation demonstrates that finding an insertion using asingle read may be difficult and generally short insertions can befound. In the next section multiple reads will be used to cover longerinsertions.

Finding an Insertion Using a Mini-Assembly

For a typical case when a long novel insertion (rather than aduplication of another region) is present in a genome. Read A overlapsB, denoted O(A,B), if a suffix of A of at least h bps long is a prefixof B. A digraph G=(V,E) is as follows: V includes all reads, there is adirect edge from A to B if O(A,B). For each read A whose prefix of atleast g bps long matches perfectly to the reference ending at positiont, call node A a starting node at position t. For each read A whosesuffix of at least g bps long matches perfectly with the referencestarting at position t call node A an ending node at position t. Aninsertion after the position t in the reference is a path from astarting node at position t to an ending node at position t+1.

The model above uses perfect matches and makes the analysis beloweasier. More generally, an overlap can be defined as approximatematches. Also note that if a prefix of a read matches to positions a,a+1, . . . , a+u of the reference genome, and u>g, it is possible thatthe insertion may start after positions a+g, a+g+1, . . . , a+u, whenthe first bases of the insertion are the same as the bases immediatelyafter the insertion. To address this issue, one can produce u−g+1 nodesfor the read corresponding to starting the insertion at position a+g, .. . , a+u. The ending positions are treated similarly.

The path described above corresponds to an insertion (to be laterdetermined if real). One proposition is: For any insertion, determinethe likelihood that sufficient reads are present cover it (i.e., find apath in G that covers the insertion). Another proposition is: Given thata path in G can be found, determine how likely it is the result ofrandom reads.

Before answering the two questions above, refine the above path findingapproach can be useful. Using paired information and looking forinsertions one region at a time may be helpful. Selecting a small region(for example, approximately 3 Kb) in the reference genome, andattempting to find insertion in this region using paired information, itis possible to find all reads that may potentially map to this region.After removing reads that map to the region with up to, for example, 6mismatches (these reads can be explained by sequencing error), one canuse the rest to form the graph G and find the path in it.

Once can also use coverage map to limit the search for insertions. Notethat, at high sequencing coverage, the place of insertion usually showsa noted drop in coverage. One can limit the search to a very smallregion surrounding those low coverage regions.

Once a path in the graph is identified, the hypothesis may be testedthat the path corresponds to a real insertion. Given a path with n nodes(reads) one may attempt to map other reads to the insertion derived fromthe path to get a consensus sequence for the insertion. This willincrease the support for the hypothesis that the insertion is real.

Again using H1 and H0 and assuming h=g. If n reads are found by theapproach described previously, P(reads|H1)=(1−p)^(Ln)/R^(n). To estimateP(reads|H0), the average analysis may be used as before. For n reads of25 bases and a random sequence of size R, from Lemma 3,P(reads|H0)=f^(n).

As in the last subsection, P(H1)=10⁻⁴8^(−S), where S is the length ofthe insertion. From the way the path is built, S<(L−h)n−h. When p=0.1,R=3000 and h=16, fR=2.54×10⁻¹⁰ and

${\frac{P\left( {H\; 1} \middle| {reads} \right)}{P\left( {H\; 0} \middle| {reads} \right)} \geq \frac{10^{- 4}\left( {1 - p} \right)^{nL}}{({fR})^{n}8^{{{({L - h})}n} - h}} \geq {2.8 \times 10^{10} \times 2.1^{n}}}\operatorname{>>}10.$

Finding a path as above with h=16, one can be confident it is true. Thecalculation indicates that when the insertion is novel, regardless ofits length, if the mini-assembly approach above can find a path to coverit, one can be very confident it is real.

The novel insertion requirement enables use of Lemma 3 to estimateP(reads|H0). In practice, it can be estimated directly by mapping allthe reads to the region allowing many mismatches. Then it can becalculated that P(H1 |reads)/P(H0|reads) and decide whether to acceptthe H1, so even if the inserted sequence is “somewhat” related to theneighboring region, the ratio may still be large enough to accept theinsertion. When the path is found, a large part of the insertion may becovered by only three reads. A post-processing step may be performed byaligning other reads to this new inserted sequence allowing some numberof mismatches. These will lead to a multiple alignment of reads and astandard base calling routine can be used to do the base-calling(similar to that of de novo sequencing).

In evaluating if there is an insertion, how likely is it that themini-assembly routine can identify it one may assume to have aninsertion of length S. It may be seen that for each position of theinsertion, there is at least one read that is perfect (no sequencingerror) that contains the position in its interior excluding the last 8bps from both ends, then there is a path covering it. If the genome sizeis G, and there are KG perfect reads, then the chance that this happensis at least q^(S),

where q=1−(1−1/G)^(KG)≈1−e^(9K).

When K=1, q¹⁰⁰⁰=0.8838 and q⁵⁰⁰=0.94. When K=0.5, there is a 57% chancethat one can find an insertion of 50 bps. This implies that highcoverage is desirable. In order to find a 1000 bp long insertion,approximately 25× base coverage by perfect reads is desired. Typically,at an error rate of 5%, only about a quarter of all 25-base reads areperfect and thus about 100× base coverage may be desired which is withinreach of AB SOLiD sequencing technology.

In summary, the mini-assembly for insertions is likely to be successfulin finding an insertion if one exists and the assembly is also a helpfulindication if an insertion exists and is real. In various embodiments animplied assumption is that the insertion detected is significantlysmaller than the average distance between paired reads. One finalchallenge in detecting insertions is that frequently an insertion is aduplication of the existing genome (it is possible that most insertionsare duplications). For this class of insertions, one can further divideit into two types: one is the case in which the insertion is aduplication of another region far away from the inserted place (>6 kpaway, e.g., jumping gene). For this type, the approach and analysisdescribed above works well. Using pairing information, one can separatereads that cover the two duplicated regions and then the insertion isnovel in its neighborhood of about 6 K bp.

Software Implementation for finding alignments with a single insertionor deletion

Earlier sections discussing the statistics of finding indels addressedfinding the optimal alignment (minimum number of mismatches) with oneinsertion or one deletion. Here the size of the insertion or deletionwas set to be smaller than certain user specified limits (insertions anddeletions may have different limits). In this section, an efficientsoftware implemented approach to finding such alignments is presented.

Given the following: let ins be the maximum size of an insertion, del bethe maximum size of a deletion and nmis be the maximum number ofmismatches allowed. Find the optimum alignment (with minimum number ofmismatches) between a read and a substring of a region of the referencesequence, such that it has at most one deletion smaller than del OR oneinsertion with size smaller than ins but not both, AND the number ofmismatches is at most nmis.

Discussed is an approach for finding the best alignment with onedeletion. The best alignments with one insertion can be found similarly.In one aspect the idea is to find gap-free alignments for each flankingsequence. Defining top-half alignments as gap-free alignments startingfrom the one portion of a flanking region and bottom-half alignmentsstart at the other portion of a flanking region. One may note that analignment with one deletion is a joining of one top-half and onebottom-half alignment.

To present the data analysis approach, an abstract data structure isintroduced referred to as a dominating list of half alignments. If A andB are two top-half alignments with the same number of mismatches andthey are at flanking regions a and b, respectively, call B dominating Aif and only if a<b and B is longer that A. For any set of halfalignments X with the same number of mismatches, a dominating subset ofX includes all half alignments h in X such that no other half alignmentin X dominates h. Design a dynamic data structure called dominating listS which maintains the dominating subset of a dynamic set of halfalignments with a fixed number of mismatches.

S supports three operations: (1) insertion where a new top-halfalignment is inserted to the set; (2) findmin which return the longestalignment in the set; and (3) deletemin which removes the longestalignment in S. Note that insert will also remove some alignments in theset which are dominated by the new alignment. The below discussiondescribes the approach using this abstract data type and then give anefficient implementation of it that gives a linear time algorithm of thetotal number of half alignments.

The approach processes flanking regions from 1 to R. Let k=nmis, andmaintain k+1 lists S0 . . . Sk for top alignments with 0 to kmismatches. At the time when processing flanking region j, store all thedominating top alignments at flanking regions<j and with i mismatches inSi. At flanking region j, find all the top alignments with up to kmismatches and insert them in the list with the correct number ofmismatches. Also find all the bottom alignments with up to k mismatchesand try to combine them with top half alignments in S to form analignment with one deletion.

The outline of the approach is described in the following pseudocode:

Approach deletion: 1. Let S0, S1...,Sk be empty 2. For flanking region j=1 to R do a. For each top alignment A at flanking region j with m<=kmismatches do i. Insert it to Sm; b. For each Sp, repeat deletemin ifthe longest half alignments are too far away such that too large of adeletion will be introduced. c. For each bottom alignment B at flakingregion j with m<=k mismatches do i. For p=0 to k-m do 1. X=findmin(Sp);2. if length of X plus length of B is larger than read length a. reportan alignment with m+p mismatches and then break (from the inner forloop); Note that at step 2.c.i.2.a, report an alignment with onedeletion. Keeping track of all the reported alignments and maintainingthe one with the minimum number of mismatches at the end of the program,reports the best alignment.

Analysis of the Running Time

The running time comprises the time to compute all of the halfalignments at steps 2.a and 2.c, and the time to process thesealignments at steps 2.c.i and 2.a.i. For random sequences, the averagetime to find all top alignments or all bottom alignments of up to kmismatches depends on the average length of alignments with k mismatches(since on a flanking region to find half alignment with k mismatchesmust first find ones with 0, 1, . . . , k−1 mismatches) and isO(4kR/3)=O(kR).

For a set of dominating top alignments, sorting the alignments byincreasing flanking regions, the lengths of the alignments decrease.Processing alignments by flanking regions, the alignments are insertedin increasing order of flanking regions. An implementation of the datatype Sj is to use a two-directional linked list ordered by flankingregion. Then findmin and deletemin can be done in O(1) time. Insertionis performed by visiting the alignment from the back of the linked listand removing alignments with shorter length until either the head of thelist is reached or one with a longer length is found. Since eachalignment can be deleted once the total time on step 2.a.i is O(kR).Similarly, total time at step 2.b is O(kR).

It initially appears that the loop at 2.c.i takes O(k) time each, butsince the length of the longest alignment of Sj decreases when jincreases and step 2.c will visit at most k+1 alignments (one each with0, 1, . . . , k mismatches) with increasing lengths, the whole step 2.ccan be implemented as a merge between two ordered lists (by length) ofk+1-item, so the total time on step 2.c is O(kR). Now the total time ofthe approach is O(kR). This compares favorably to O(LR) for traditionalSW-type alignments (Smith and Waterman 1990, Needleman and Wunsh 1970)as noted that the typical value for k is 0-2 and L could be 30-50.

All publications and patent applications mentioned in this specificationare indicative of the level of skill of those skilled in the art towhich this invention pertains. All publications and patent applicationsare herein incorporated by reference to the same extent as if eachindividual publication or patent application was specifically andindividually indicated to be incorporated by reference.

The systems, methods, and analytical approaches of the current teachingshave been described broadly and generically herein. Each of the narrowerspecies and sub-generic groupings falling within the generic disclosurealso form part of the current teachings. This includes the genericdescription of the current teachings with a proviso or negativelimitation removing any subject matter from the genus, regardless ofwhether or not the excised material is specifically recited herein.

Although the disclosed teachings has been described with reference tovarious applications, methods, and compositions, it will be appreciatedthat various changes and modifications may be made without departingfrom the teachings herein. The foregoing examples are provided to betterillustrate the present teachings and are not intended to limit the scopeof the teachings herein. Certain aspects of the present teachings may befurther understood in light of the following claims.

1. A method of nucleic acid sequence analysis, comprising: receivingnucleic acid sequence information comprising one or more mate pairsequences, wherein mate pair sequences comprise non-overlapping pairwisesequences separated by an intervening sequence length; receiving nucleicacid sequence information comprising at least one reference sequence;performing a mapping operation for each of the one or more mate pairsequences in which the non-overlapping pairwise sequences are aligned tothe at least one reference sequence by the steps of: performing a firstmapping operation aligning the non-overlapping pairwise sequences to theat least one reference sequence with a selected mismatch constraintidentifying non-overlapping pairwise sequences for which one of thenon-overlapping pairwise sequences align to the at least one referencesequence while satisfying the selected mismatch constraint; performing asecond mapping operation designating a window region of the referencesequence to align the non aligned pairwise sequence with a selectedmismatch constraint; identifying non-overlapping pairwise sequences thatare successfully mapped following performing the first and secondmapping operations; and, outputting the results of the mappingoperations.
 2. The method of claim 1, wherein the second mappingoperation further identifies indels with respect to the referencesequence by determining a difference between an expected interveningsequence length between the non-overlapping pairwise sequences and anobserved intervening sequence length between the non-overlappingpairwise sequences.
 3. The method of claim 2, wherein the indelcomprises an insertion occurring between the non-overlapping pairwisesequences which accounts for the difference between the expectedintervening sequence length and the observed intervening sequencelength.
 4. The method of claim 2, wherein the indel comprises a deletionoccurring between the non-overlapping pairwise sequences which accountsfor the difference between the expected intervening sequence length andthe observed intervening sequence length.
 5. The method of claim 1,wherein the non-overlapping sequence information comprises paired readsequence information separated by the intervening sequence whosedistance is approximately known.
 6. The method of claim 5, wherein eachof the paired read sequences has a length of between approximately 10and 75 bases.
 7. The method of claim 5, wherein the intervening sequencehas a length of between approximately 2 kilobases and 15 kilobases.
 8. Asystem for nucleic acid sequence analysis, comprising: a data analysisunit configured to: receive nucleic acid sequence information for one ormore mate pair sequences, wherein mate pair sequences comprisenon-overlapping pairwise sequences separated by an intervening sequencelength and further configured to receive nucleic acid sequenceinformation for at least one reference sequence; perform a mappingoperation for each of the one or more mate pair sequences in which thenon-overlapping pairwise sequences are aligned to the at least onereference sequence by the steps of: performing a first mapping operationaligning the non-overlapping pairwise sequences to the at least onereference sequence with a selected mismatch constraint identifyingnon-overlapping pairwise sequences for which one of the non-overlappingpairwise sequences align to the at least one reference sequence whilesatisfying the selected mismatch constraint; performing a second mappingoperation designating a window region of the reference sequence to alignthe non aligned pairwise sequence with a selected mismatch constraint;identify non-overlapping pairwise sequences that are successfully mappedfollowing performing the first and second mapping operations; and, adata terminal for displaying the results of the mapping operationsgenerated by the data analysis unit to a user.
 9. The system of claim 8,wherein the second mapping operation performed by the data analysis unitfurther identifies indels with respect to the reference sequence bydetermining a difference between an expected intervening sequence lengthbetween the non-overlapping pairwise sequences and an observedintervening sequence length between the non-overlapping pairwisesequences.
 10. The system of claim 9, wherein the indel comprises aninsertion occurring between the non-overlapping pairwise sequences whichaccounts for the difference between the expected intervening sequencelength and the observed intervening sequence length.
 11. The system ofclaim 9, wherein the indel comprises a deletion occurring between thenon-overlapping pairwise sequences which accounts for the differencebetween the expected intervening sequence length and the observedintervening sequence length.
 12. The system of claim 8, wherein thenon-overlapping sequence information comprises paired read sequenceinformation separated by the intervening sequence whose distance isapproximately known.
 13. The system of claim 12, wherein each of thepaired read sequences has a length of between approximately 10 and 75bases.
 14. A computer-readable medium, the computer-readable mediumbeing readable to execute a method of nucleic acid sequence analysis,the method comprising: receiving nucleic acid sequence informationcomprising one or more mate pair sequences, wherein mate pair sequencescomprise non-overlapping pairwise sequences separated by an interveningsequence length; receiving nucleic acid sequence information comprisingat least one reference sequence; performing a mapping operation for eachof the one or more mate pair sequences in which the non-overlappingpairwise sequences are aligned to the at least one reference sequence bythe steps of: performing a first mapping operation aligning thenon-overlapping pairwise sequences to the at least one referencesequence with a selected mismatch constraint identifying non-overlappingpairwise sequences for which one of the non-overlapping pairwisesequences align to the at least one reference sequence while satisfyingthe selected mismatch constraint; performing a second mapping operationdesignating a window region of the reference sequence to align the nonaligned pairwise sequence with a selected mismatch constraint;identifying non-overlapping pairwise sequences that are successfullymapped following performing the first and second mapping operations;and, outputting the results of the mapping operations.
 15. Thecomputer-readable medium of claim 14, wherein the second mappingoperation further identifies indels with respect to the referencesequence by determining a difference between an expected interveningsequence length between the non-overlapping pairwise sequences and anobserved intervening sequence length between the non-overlappingpairwise sequences.
 16. The computer-readable medium of claim 15,wherein the indel comprises an insertion occurring between thenon-overlapping pairwise sequences which accounts for the differencebetween the expected intervening sequence length and the observedintervening sequence length.
 17. The computer-readable medium of claim15, wherein the indel comprises a deletion occurring between thenon-overlapping pairwise sequences which accounts for the differencebetween the expected intervening sequence length and the observedintervening sequence length.
 18. The computer-readable medium of claim14, wherein the non-overlapping sequence information comprises pairedread sequence information separated by the intervening sequence whosedistance is approximately known.
 19. The computer-readable medium ofclaim 18, wherein each of the paired read sequences has a length ofbetween approximately 10 and 75 bases.
 20. The computer-readable mediumof claim 19, wherein the intervening sequence has a length of betweenapproximately 2 kilobases and 15 kilobases.